Latent Twitter Image Information for Social Analytics

Abstract

The appearance of images in social messages is continuously increasing, along with user engagement with that type of content. Analysis of social images can provide valuable latent information, often not present in the social posts. In that direction, a framework is proposed exploiting latent information from Twitter images, by leveraging the Google Cloud Vision API platform, aiming at enriching social analytics with semantics and hidden textual information. As validated by our experiments, social analytics can be further enriched by considering the combination of user-generated content, latent concepts, and textual data extracted from social images, along with linked data. Moreover, we employed word embedding techniques for investigating the usage of latent semantic information towards the identification of similar Twitter images, thereby showcasing that hidden textual information can improve such information retrieval tasks. Finally, we offer an open enhanced version of the annotated dataset described in this study with the aim of further adoption by the research community.

Keywords

• social labeling
• Twitter
• images
• Google Cloud Vision API
• OCR
• cosine similarity